Improving genetic predictions for complex diseases for everyone
Official title: Making Genomic Prediction of Complex Disease Equitable
Funded at University of Texas at Austin · NIH-10896318 · Data updated 2026-11-12
This study is working on improving how we use genetic information to predict complex diseases, especially for people from diverse backgrounds, so that everyone can benefit from better health insights.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas at Austin NIH-funded |
| Lab location | 1 site (Austin, United States) |
| Project start | August 1, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $396,250 |
| NIH project number | 5R35GM151108-02 |
| Project ID | NIH-10896318 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals from historically underserved and underrepresented groups who may benefit from improved genetic predictions.
✕Less likely to help
Patients who do not have complex genetic traits or those from populations already well-represented in existing GWAS may not receive significant benefits from this research.
What this research studies
This research focuses on enhancing the accuracy of genetic predictions related to complex diseases by developing new statistical methods. It aims to address the limitations of current polygenic scores, which often perform poorly in diverse populations. By combining data from Genome-Wide Association Studies (GWAS) with advanced population genetics tools, the project seeks to better understand how genetic and environmental factors interact in influencing health outcomes. The ultimate goal is to ensure that these genetic insights benefit historically underserved and underrepresented groups.
Why it matters
Potential benefit: If successful, this research could lead to more accurate genetic predictions for complex diseases, improving health outcomes for diverse populations.
How similar studies have gone: Other research has shown promise in improving genetic prediction methods, but this approach aims to address specific gaps in representation and accuracy, making it a novel endeavor.
Where this research is happening
-
University of Texas at Austin
Austin, United States Active
Researchers
- Principal investigator: Harpak, Arbel — University of Texas at Austin
- Study coordinator: Harpak, Arbel
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.